Introduction

Data from our collaborator Panagiotis rds file for a SingleCellExperiment object containing the single cell data for the interstitial cells of Hydra Vulgaris during multiples stages of regeneration after bisection:

https://www.dropbox.com/scl/fi/dg76sigjnj5u6qr06xk79/sce_interstitial_Juliano.rds?rlkey=f0y3lqt0wdcwq652zisnrpf9t&dl=0

BUT they mapped it to Hydra Magnipapillata (102 version of 105) “Quantification of the generated single cell libraries was performed using the Salmon-Alevin software suite (Salmon version 1.6.0) against the ncbi Hydra 102 transcriptome.”

The coldata of the object contain cell annotation including

The rowdata contains gene annotation, using Entrez-gene identifiers. I have also noticed that in the sce objects there’s

I converted the sce objects 6.6gb into a seurat object 2.2 gb and checked that all cited parameters could be found in it.

Summary of 3/06 meeting with Hannah and Celina

Data loading and upate on the project

This report is a repeat of report 5 but addresses the above-mentioned comments from the meeting with Celina and Hannah.

In this report we’re gonna plot transcription factors of interest over the different timepoints. I also wanted to add cell population counts over time. Maybe I could learn to plot gene expression over time too? Anyway, we’re exploring gene expression programs and cell populations over time.

Changing UMAP resolution

target <- c("NSP4","midasin","mini-COL8","SP-D-like","FH20-X3","CAII",
            "CELA3B","zinc-carboxypeptidase","ANO39","TYN1","H2A.2.2","nas2-X2",
            "Lwamide-X1","DMRT1","TUBA4A-X1","HTRA3","polRF-X1","ec3A","ec3B",
            "nop58-X1","OTP","MEP1A","rhammosyl-O-methyltransferase","hywnt3",
            "PPOD1","ks1","hyAlx","ELAV2","POU4","MUC2", "ec3", "grm1","myc",
            "myc1","wnt3","ec2","ec1B","ec4","ec1A","en1","en1_NDF1_DANRE",
            "ec5","en1","en2A","en2B","myb") 

I founda myb by ctrl+f in the annotation file. However, in Stefan’s supplementary analysis, myb is described as “g27424” after blasting, I only got a 32% hit (80% cover) in the 102 annotation file…

Uncharacterized protein LOC105848384 [Hydra vulgaris] Hydra vulgaris 243 243 82% 2e-68 32.09% 1023 XP_047129678.1

Subsetting by head/foot and timepoint

Pseudo axis values

Cell cycle values

Apoptosis score

Cell population evolution over time

Regenerating a head

“Raw” cell numbers

REG_HEAD_t0 REG_HEAD_t06 REG_HEAD_t12 REG_HEAD_t24 REG_HEAD_t48 REG_HEAD_t96
earlyGc 684 574 129 208 278 253
earlyNem 202 209 148 84 128 78
earlyNeur 59 65 125 96 102 78
ec1A 67 55 48 64 50 112
ec1A/ec1B 0 0 0 0 3 58
ec2 0 0 0 0 1 56
ec3 76 64 71 80 65 168
ec4 0 0 0 2 6 71
en 36 27 29 18 24 56
Gc 64 73 66 60 95 115
GranG/ZymoG 256 246 204 212 223 320
ISC 488 388 420 201 339 384
Nb 1549 1398 870 553 737 1288
Sum 3481 3099 2110 1578 2051 3037

Relative number of cells

REG_HEAD_t0 REG_HEAD_t06 REG_HEAD_t12 REG_HEAD_t24 REG_HEAD_t48 REG_HEAD_t96
earlyGc 19.65 18.52 6.11 13.18 13.55 8.33
earlyNem 5.80 6.74 7.01 5.32 6.24 2.57
earlyNeur 1.69 2.10 5.92 6.08 4.97 2.57
ec1A 1.92 1.77 2.27 4.06 2.44 3.69
ec1A/ec1B 0.00 0.00 0.00 0.00 0.15 1.91
ec2 0.00 0.00 0.00 0.00 0.05 1.84
ec3 2.18 2.07 3.36 5.07 3.17 5.53
ec4 0.00 0.00 0.00 0.13 0.29 2.34
en 1.03 0.87 1.37 1.14 1.17 1.84
Gc 1.84 2.36 3.13 3.80 4.63 3.79
GranG/ZymoG 7.35 7.94 9.67 13.43 10.87 10.54
ISC 14.02 12.52 19.91 12.74 16.53 12.64
Nb 44.50 45.11 41.23 35.04 35.93 42.41
Sum 100.00 100.00 100.00 100.00 100.00 100.00

Notice how early Gc decrease almost 5x between 12-24 hours while ISC, GranG and early neurons spike up 2 to 3X. I think that population is called upon to create not only gland cells but can help support other progenitors.

Regenerating a foot

“Raw” cell numbers

REG_FOOT_t0 REG_FOOT_t06 REG_FOOT_t12 REG_FOOT_t24 REG_FOOT_t48 REG_FOOT_t96
earlyGc 60 41 38 51 204 85
earlyNem 200 266 211 236 86 205
earlyNeur 199 201 176 143 66 100
ec1A 51 57 42 54 36 55
ec1A/ec1B 59 77 58 51 64 55
ec2 80 103 77 63 84 51
ec3 91 87 94 75 56 68
ec4 49 48 46 43 43 32
en 38 31 28 22 27 19
Gc 149 152 143 131 65 93
GranG/ZymoG 332 374 280 269 189 196
ISC 581 452 401 390 320 287
Nb 948 790 731 797 559 713
Sum 2837 2679 2325 2325 1799 1959

Relative number of cells

REG_FOOT_t0 REG_FOOT_t06 REG_FOOT_t12 REG_FOOT_t24 REG_FOOT_t48 REG_FOOT_t96
earlyGc 2.11 1.53 1.63 2.19 11.34 4.34
earlyNem 7.05 9.93 9.08 10.15 4.78 10.46
earlyNeur 7.01 7.50 7.57 6.15 3.67 5.10
ec1A 1.80 2.13 1.81 2.32 2.00 2.81
ec1A/ec1B 2.08 2.87 2.49 2.19 3.56 2.81
ec2 2.82 3.84 3.31 2.71 4.67 2.60
ec3 3.21 3.25 4.04 3.23 3.11 3.47
ec4 1.73 1.79 1.98 1.85 2.39 1.63
en 1.34 1.16 1.20 0.95 1.50 0.97
Gc 5.25 5.67 6.15 5.63 3.61 4.75
GranG/ZymoG 11.70 13.96 12.04 11.57 10.51 10.01
ISC 20.48 16.87 17.25 16.77 17.79 14.65
Nb 33.42 29.49 31.44 34.28 31.07 36.40
Sum 100.00 100.00 100.00 100.00 100.00 100.00

Notice how early neurons only go up after a spike in earlGc. Is the opposite happening here?

Transcription factors

We are looking at transcription factors of interest that, based on bulk RNAseq, appeared active only in the injured animal, not the homeostatic one.

Dotplot

Feature plot

Regenerating a head

It looks like the appearing cells might be the one with the transcription factor activity

Regenerating a foot

Session info

## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggpubr_0.5.0       scCustomize_1.1.1  Seurat_5.0.1       SeuratObject_5.0.0
##  [5] sp_1.5-1           openxlsx_4.2.5.1   forcats_0.5.2      stringr_1.4.1     
##  [9] dplyr_1.0.10       purrr_0.3.5        readr_2.1.3        tidyr_1.2.1       
## [13] tibble_3.1.8       ggplot2_3.4.0      tidyverse_1.3.2   
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2             spatstat.explore_3.0-5 reticulate_1.26       
##   [4] tidyselect_1.2.0       htmlwidgets_1.5.4      grid_4.2.2            
##   [7] Rtsne_0.16             munsell_0.5.0          codetools_0.2-18      
##  [10] ica_1.0-3              future_1.29.0          miniUI_0.1.1.1        
##  [13] withr_2.5.0            spatstat.random_3.0-1  colorspace_2.0-3      
##  [16] progressr_0.11.0       highr_0.9              knitr_1.41            
##  [19] rstudioapi_0.14        stats4_4.2.2           ROCR_1.0-11           
##  [22] ggsignif_0.6.4         tensor_1.5             listenv_0.8.0         
##  [25] labeling_0.4.2         polyclip_1.10-4        farver_2.1.1          
##  [28] parallelly_1.32.1      vctrs_0.5.0            generics_0.1.3        
##  [31] xfun_0.34              timechange_0.1.1       R6_2.5.1              
##  [34] doParallel_1.0.17      clue_0.3-62            ggbeeswarm_0.7.2      
##  [37] spatstat.utils_3.0-1   cachem_1.0.6           assertthat_0.2.1      
##  [40] promises_1.2.0.1       scales_1.2.1           googlesheets4_1.0.1   
##  [43] beeswarm_0.4.0         gtable_0.3.1           globals_0.16.2        
##  [46] goftest_1.2-3          spam_2.10-0            rlang_1.0.6           
##  [49] GlobalOptions_0.1.2    splines_4.2.2          rstatix_0.7.1         
##  [52] lazyeval_0.2.2         gargle_1.2.1           spatstat.geom_3.0-3   
##  [55] broom_1.0.1            yaml_2.3.6             reshape2_1.4.4        
##  [58] abind_1.4-5            modelr_0.1.10          backports_1.4.1       
##  [61] httpuv_1.6.6           tools_4.2.2            ellipsis_0.3.2        
##  [64] jquerylib_0.1.4        RColorBrewer_1.1-3     BiocGenerics_0.44.0   
##  [67] ggridges_0.5.4         Rcpp_1.0.9             plyr_1.8.8            
##  [70] deldir_1.0-6           pbapply_1.6-0          GetoptLong_1.0.5      
##  [73] cowplot_1.1.1          S4Vectors_0.36.0       zoo_1.8-11            
##  [76] haven_2.5.2            ggrepel_0.9.2          cluster_2.1.4         
##  [79] fs_1.5.2               magrittr_2.0.3         data.table_1.14.4     
##  [82] RSpectra_0.16-1        scattermore_1.2        circlize_0.4.15       
##  [85] lmtest_0.9-40          reprex_2.0.2           RANN_2.6.1            
##  [88] googledrive_2.0.0      fitdistrplus_1.1-8     matrixStats_0.62.0    
##  [91] hms_1.1.2              patchwork_1.1.2        mime_0.12             
##  [94] evaluate_0.18          xtable_1.8-4           readxl_1.4.1          
##  [97] IRanges_2.32.0         fastDummies_1.7.3      gridExtra_2.3         
## [100] shape_1.4.6            compiler_4.2.2         KernSmooth_2.23-20    
## [103] crayon_1.5.2           htmltools_0.5.3        later_1.3.0           
## [106] tzdb_0.3.0             ggprism_1.0.4          lubridate_1.9.0       
## [109] DBI_1.1.3              dbplyr_2.2.1           ComplexHeatmap_2.14.0 
## [112] MASS_7.3-58.1          Matrix_1.6-1.1         car_3.1-1             
## [115] cli_3.4.1              parallel_4.2.2         dotCall64_1.1-0       
## [118] igraph_1.3.5           pkgconfig_2.0.3        plotly_4.10.1         
## [121] spatstat.sparse_3.0-0  xml2_1.3.3             paletteer_1.5.0       
## [124] foreach_1.5.2          vipor_0.4.5            bslib_0.4.1           
## [127] rvest_1.0.3            snakecase_0.11.0       digest_0.6.30         
## [130] sctransform_0.4.1      RcppAnnoy_0.0.20       janitor_2.2.0         
## [133] spatstat.data_3.0-0    rmarkdown_2.18         cellranger_1.1.0      
## [136] leiden_0.4.3           uwot_0.1.14            shiny_1.7.3           
## [139] rjson_0.2.21           lifecycle_1.0.3        nlme_3.1-160          
## [142] jsonlite_1.8.3         carData_3.0-5          viridisLite_0.4.1     
## [145] fansi_1.0.3            pillar_1.9.0           lattice_0.20-45       
## [148] ggrastr_1.0.2          fastmap_1.1.0          httr_1.4.4            
## [151] survival_3.4-0         glue_1.6.2             zip_2.2.2             
## [154] png_0.1-7              iterators_1.0.14       stringi_1.7.8         
## [157] sass_0.4.2             rematch2_2.1.2         RcppHNSW_0.5.0        
## [160] irlba_2.3.5.1          future.apply_1.10.0